Multiple-Input Multiple Output (MIMO) technology is employed in today's wireless digital communication systems to improve spectral-efficiency and robustness to fading without increasing power or bandwidth. In many current wireless standards, MIMO may be combined with channel coding to further improve the system diversity. Besides, Quadrature Amplitude Modulation (QAM) may be utilized to further increase spectral-efficiency. One challenge in MIMO is the detection stage, which is performed at the receiver and can require an excessively large computational complexity in order to achieve the optimal MIMO gain. As of today, many detection techniques have been proposed to closely approach optimal performance with affordable complexity. Among them, K-best detection methods offer an excellent performance/complexity tradeoff.
K-best detection methods search in a breadth-first manner a MIMO detection tree configuration, wherein, the tree configuration is formed by a plurality of nodes arranged in levels, and connected via a plurality of branches. Basically, for each level in the tree configuration, the K-best detection algorithm only expands the paths emerging from the K nodes with the smallest metric.
Historically, the first K-best detector implementations have been based on the Fincke-Pohst (FP) and Schnorr-Euchner (SE) strategies originally used in sphere decoding. However, these strategies may not yield the best complexity-efficiency since they involve complex operations such as matrix inversion in the preprocessing stage. More recently, one observed that, although utilized in sphere decoding, matrix inversions may be unnecessary in K-best detection, Accordingly, many implementations may utilize K-best detection algorithms involving no such inversions. Moreover, given the lattice property of QAM constellations, the computational complexity utilized to detect each signal can be further reduced by replacing some multiplications with shift/add operations.
Despite the momentum to reduce K-best detection complexity, all K-best detection techniques proposed so far can be identified to a K-best tree search, where the complexity associated with visiting a node in the tree configuration grows with the node depth (i.e., level) in the tree.